Real Fluid Modeling and Simulation of the Structures and Dynamics of Condensation in CO2 Flows Shocked Inside a de Laval Nozzle, Considering the Effects of Impurities
Bibliographic record
Abstract
Because of the currently changing climate, Carbon Capture and Storage (CCS) is increasingly becoming an important contemporary topic. However, this technique still faces various challenges. For the compression of CO2 to its supercritical condition for efficient transport, one of the important challenges is mastering the two-phase flow in the pump. Indeed, phase changes that appear on the blade tips of an impeller or rotor in such pumps can lead to performance and stability issues. Moreover, these phase change phenomena (vaporization and condensation) can be significantly modified by the presence of impurities (N2, O2, H2S, etc.) whose nature depends on the source of the CO2 production. In this work, we focus on analyzing the high pressure flow behavior of CO2 mixed with varying levels of impurities in a de Laval nozzle, for which experimental results are available. Numerical simulations are performed using a real-fluid model (RFM) implemented in the CONVERGE CFD solver. In this model, a tabulation approach is used to provide the thermodynamic and transport properties of the mixture of CO2 with the impurities. The study is carried out with different inlet conditions, and the results are in good agreement with the available experimental data. In addition, the results provide insights on the interaction of the shock wave with the observed condensation phenomenon, as well as its impact on the amount of condensation and other thermodynamic variables. The research indicates that the presence of impurities mixed with CO2 significantly affects the observed condensation in gas streams, which is a crucial factor that cannot be overlooked when implementing CCS systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".